Summary of Explainable Ai Needs Formal Notions Of Explanation Correctness, by Stefan Haufe et al.
Explainable AI needs formal notions of explanation correctness
by Stefan Haufe, Rick Wilming, Benedict Clark, Rustam Zhumagambetov, Danny Panknin, Ahcène Boubekki
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper highlights the limitations of popular Explainable Artificial Intelligence (XAI) methods in providing quality control for Machine Learning (ML) systems. Despite being touted as a solution to make ML decisions human-understandable, XAI methods fail to reliably answer important questions about ML models, their training data, or test inputs. The authors demonstrate that these methods systematically attribute importance to input features independent of the prediction target, limiting their utility for tasks like model validation and scientific discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that popular XAI methods don’t work well in making decisions understandable. These methods can’t answer important questions about ML models or their data. The authors found that these methods say which input features are most important, but this doesn’t help us understand how the ML model works. This is a problem because we need to be able to explain why an ML system made a decision. |
Keywords
» Artificial intelligence » Machine learning
